# Fine mapping rheumatic disease variants using functional genomic sequencing

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2020 · $348,700

## Abstract

PROJECT SUMMARY ABSTRACT
Here, we propose to develop a two-step computational strategy to improve the power and resolution of
identifying non-coding variants causal for autoimmune rheumatic disease by integrating functional genomic
data. The computational methods developed here address an important problem in disease biology:
pinpointing the precise disease-causing mutations implicated by genome-wide association studies (GWAS)
and understanding the biological mechanisms by which they act. We will develop our program using activated
CD4+ T cells as a model system because of their relevance to autoimmune rheumatic disease, the availability
of functional genomic data, and the ability to experimentally manipulate primary T cells and related cell lines.
The three overlapping aims are:
1. Leveraging allele-specific reads to increase the power of detecting functional genomic quantitative
trait loci (fgQTLs). We will (i) develop an approach to accurately quantify allele-specific reads from functional
genomic sequencing data while accounting for sequencing and mapping biases, (ii) develop a linear mixed
model (LMM) method to perform phase-aware association tests for functional genomic traits, and (iii) apply the
method to identify expression and chromatin accessibility QTLs in activated CD4+ T cells in ~100 individuals.
2. Nominate causal non-coding variants in autoimmune rheumatic disease-associated loci. We will (i)
develop a method that leverages functional genomic QTLs to fine map disease-causing variants in a locus, (ii)
apply the method to integrate expression and chromatin accessibility QTLs from Aim 1 with three autoimmune
rheumatic disease GWAS datasets to identify disease-causing variants most likely associated with CD4+ T cell
activation, (iii) computationally refine and annotate causal variants using orthogonal functional genomic data in
CD4+ T cells.
3. Validate predictions using synthetic biology and genome engineering. We will (i) use massively
parallel reporter assays (MPRAs) to test in activated Jurkats, ~500 synthetic constructs harboring predicted
causal variants from Aims 1 and 2 prioritized for GWAS loci, and use CRISPR/Cas9 to (ii) knock out 25
enhancers harboring causal variants (a subset of the MPRA hits) in Jurkats and CD4+ primary T cells and (iii)
knock-in 10 predicted causal variants in CD4+ primary T cells. We will observe the endogenous effects of
genome edits by profiling molecular and cellular phenotypes during CD4+ T cell activation and differentiation.

## Key facts

- **NIH application ID:** 9906757
- **Project number:** 5R01AR071522-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Chun Jimmie Ye
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $348,700
- **Award type:** 5
- **Project period:** 2017-05-01 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9906757

## Citation

> US National Institutes of Health, RePORTER application 9906757, Fine mapping rheumatic disease variants using functional genomic sequencing (5R01AR071522-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9906757. Licensed CC0.

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